AI Product Recommendations: How They Work and Where to Use Them

“You may also like” is the most ignored real estate in ecommerce, and it is not the shopper’s fault. Most recommendation blocks show the same six bestsellers on every page, suggest a phone case for a phone the customer just bought, or promote something that went out of stock a week ago.

AI product recommendations fix that by choosing per context and per shopper rather than per template. This guide covers how the engines actually work, the four types of recommendation and where each one belongs, the merchandising layer that decides whether they make money, and how to measure them honestly.

What’s in this guide

1. What makes a recommendation “AI”
2. How recommendation engines work
3. The four types of recommendation
4. Where each type belongs
5. The merchandising layer
6. Measuring recommendation revenue
7. Implementing them on Shopify
8. FAQ

What Makes a Recommendation “AI”

A recommendation block is only as good as the logic choosing its contents. Three generations exist in the wild, often on the same store:

• Manual: someone picked these products, once. Fine for a curated capsule, stale within a month.
• Rule-based: “show other items in the same collection.” Predictable and cheap, but blind to the shopper and to performance, which is why it produces the same six products everywhere.
• AI-driven: the engine learns which products relate to which, and which of those relationships apply to this shopper in this context, updating continuously as behaviour and stock change.

The difference shows up in the boring cases: a rule-based block cheerfully recommends a second identical product; an AI engine has learned that nobody buys two.

How Recommendation Engines Work

You do not need to build one, but knowing the approaches explains why a given block behaves as it does.

• Collaborative filtering: “shoppers who bought this also bought that.” Powerful once you have volume, and weak on brand-new products nobody has interacted with yet.
• Content-based: matches on product attributes, category, material, style, price band, so it works from day one and handles new arrivals, but only understands what your catalog data actually says.
• Behavioral and sequence models: read the current session, what was viewed, searched, skipped, to infer intent right now, which is what makes recommendations feel responsive rather than generic.
• Hybrid: the approach real systems use, blending the above so each covers the others’ blind spots, then re-ranking the candidates before display.

Two consequences worth holding onto. First, cold start is real: new stores and new products lean on content-based logic until behaviour accumulates. Second, everything degrades with poor catalog data, because attributes are what the engine reasons over.

The Four Types of Recommendation

Whatever the engine, the output falls into four types, and they answer different shopper questions.

The type has to match the moment, and everything underneath has to respect your stock and margin.

• Similar: “more like this.” For a shopper still comparing, useful on product pages and after a search that nearly worked.
• Complementary: “goes with this.” The cross-sell that raises basket size, and the only type that belongs near the cart, because similar items there just reopen a decision already made.
• Behavioral: “others also bought.” Social proof with genuine predictive value once you have data volume.
• Personalized: “picked for you,” drawn from this shopper’s own history and session. The most valuable and the most demanding, because it needs the data to be real rather than decorative.

Where Each Type Belongs

• Product page: similar items for comparison, plus complementary items for the obvious pairing. Keep them visually separate so the shopper reads them as two different offers.
• Cart and checkout: complementary only, low-consideration, and few. This is the highest-risk placement in the store: distraction here costs you a completed order.
• Collection pages: personalized and behavioral blocks work well above or inside a long grid, because the shopper has signalled a category but not an item.
• Post-purchase and email: the most underused surface, where personalization has the most history to work with and no risk of interrupting a checkout.
• Empty search results and 404s: instead of a dead end, a recommendation block turns an exit into a second chance.

The underlying rule: match the type to the shopper’s moment. Google Cloud’s Harris Poll research found 69% of consumers bought additional items after a successful search, the appetite for more is there, as long as what you put in front of them makes sense where they are standing.

The Merchandising Layer

Here is what separates a recommendation engine that lifts revenue from one that just lifts clicks: relevance alone is not a business rule.

A purely relevance-driven engine will happily recommend your least profitable item, your last remaining size, or a product you are trying to phase out. The engines worth running let you weight recommendations by stock depth, margin, and strategy alongside affinity, so the products that get the spotlight are ones you can actually profit from selling.

This is the same discipline as ranking a collection, applied to a smaller surface, which is why recommendations should run on the same merchandising brain as the rest of the store rather than as a separate widget with its own opinions. It is exactly how Kimonix product recommendations are built: 1:1 personalization with profit and stock rules applied, so recommendations minimise dead stock instead of ignoring it.

Measuring Recommendation Revenue

Recommendation reporting is where vendors flatter themselves, because any product clicked from a block gets credited. Measure it properly:

• Attributed revenue, but net: revenue from recommended products minus what those shoppers would likely have bought anyway. The only way to know is a holdout group.
• Average order value with and without recommendation blocks active, which is the metric cross-sells exist to move.
• Click-through and add-to-cart rate per placement, to find the blocks nobody engages with and kill them.
• Margin per order, to catch an engine quietly optimizing toward discounted stock.
• Cart-stage impact specifically: watch completion rate when checkout recommendations are on. If it dips, the block is costing more than it earns.

Implementing Them on Shopify

• Start where the money is: product page complementary blocks and post-purchase emails usually pay back fastest.
• Fix catalog data first. Attributes, categories, and accurate stock are what the engine reasons over; messy data produces confident nonsense.
• Set the merchandising rules before launch: stock thresholds, margin weighting, and any products that should never be recommended.
• Test one placement at a time with a holdout, then roll out what wins.
• Review quarterly, since assortment and season change what “goes with this” even means.

Related reads: How Online Retail Product Recommendations Affect Sales Growth and Ecommerce Cross-Selling: 7 Strategies for Shopify Stores.